Can Social Interactions Support Women in STEM Education? The Data Mining Approach

Abstract

Women are significantly underrepresented in STEM fields. Despite several studies proposed various interventions and measures to address STEM education gender inequality, these efforts are often constrained by traditional research methods and limited participant knowledge. This study addresses these gaps by leveraging data mining to analyze the roles and contributions of individuals using the hashtag #WomeninSTEM on Twitter. Analyzing 101,432 Twitter posts using social network and topic modeling analysis, this study investigates the dynamics of participant interactions and the nature of shared information within the #WomeninSTEM site. The findings reveal the #WomeninSTEM site as a disseminating information network comprising several small communities. Participants actively shared diverse information, including bolstering gender diversity, disseminating female success stories, sharing job opportunities, promoting online events, and collaborating on projects. This study provides novel insights and methodological approaches, emphasizing the critical role of online social networks in advancing women’s participation and success in STEM fields.